Abstract
This paper describes our followed methodology for the automatic extraction of disease mentions from tweets in Spanish as part of the SocialDisNER challenge within the 2022 Social Media Mining for Health Applications (SMM4H) Shared Task. We followed a Joint Learning ensemble architecture for the fine-tuning of top performing pre-trained language models in biomedical domain for Named Entity Recognition tasks. We used text generation techniques to augment training data. During practice phase of the challenge our approach showed results of 0.87 F1-Score.- Anthology ID:
- 2022.smm4h-1.20
- Volume:
- Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Graciela Gonzalez-Hernandez, Davy Weissenbacher
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 68–70
- Language:
- URL:
- https://aclanthology.org/2022.smm4h-1.20
- DOI:
- Cite (ACL):
- Kendrick Cetina and Nuria García-Santa. 2022. FRE at SocialDisNER: Joint Learning of Language Models for Named Entity Recognition. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 68–70, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- FRE at SocialDisNER: Joint Learning of Language Models for Named Entity Recognition (Cetina & García-Santa, SMM4H 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.smm4h-1.20.pdf